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Microsoft DP-100 Exam Questions

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Description

Exam Name: Designing and Implementing a Data Science Solution on Azure
Exam Code: DP-100
Related Certification(s): Microsoft Azure Data Scientist Associate Certification
Certification Provider: Microsoft
Actual Exam Duration: 100 Minutes
Number of DP-100 practice questions in our database: 442 (updated: Jan. 23, 2025)
Expected DP-100 Exam Topics, as suggested by Microsoft :

  • Module 1: Design and prepare a machine learning solution: This topic equips Microsoft Data Scientists with skills to design comprehensive machine learning solutions tailored to business needs. It delves into managing resources and assets in the Azure Machine Learning workspace, offering insights into organizing computational and storage resources effectively.
  • Module 2: Explore data, and run experiments: This section focuses on enabling Microsoft Data Scientists to explore datasets and experiment with model configurations effectively. By leveraging automated machine learning, participants explore optimal models and streamline custom model training with notebooks. It evaluates skills in automating hyperparameter tuning to refine models for improved performance.
  • Module 3: Train and deploy models: In this topic, Microsoft Data Scientists develop the expertise to execute training scripts and implement efficient training pipelines. Participants learn to manage models effectively and deploy them using Azure services. The focus is on assessing proficiency in training processes, deployment strategies, and lifecycle management of machine learning models to ensure production-ready solutions that align with organizational goals.
  • Module 4: Optimize language models for AI applications: This advanced topic teaches Microsoft Data Scientists to enhance AI applications by optimizing language models. Skills are developed in preparing models for optimization, prompt engineering, and employing techniques like Prompt Flow and Retrieval Augmented Generation (RAG). Additionally, fine-tuning strategies are explored to maximize model performance.

Description

Exam Name: Designing and Implementing a Data Science Solution on Azure
Exam Code: DP-100
Related Certification(s): Microsoft Azure Data Scientist Associate Certification
Certification Provider: Microsoft
Actual Exam Duration: 100 Minutes
Number of DP-100 practice questions in our database: 442 (updated: Jan. 23, 2025)
Expected DP-100 Exam Topics, as suggested by Microsoft :

  • Module 1: Design and prepare a machine learning solution: This topic equips Microsoft Data Scientists with skills to design comprehensive machine learning solutions tailored to business needs. It delves into managing resources and assets in the Azure Machine Learning workspace, offering insights into organizing computational and storage resources effectively.
  • Module 2: Explore data, and run experiments: This section focuses on enabling Microsoft Data Scientists to explore datasets and experiment with model configurations effectively. By leveraging automated machine learning, participants explore optimal models and streamline custom model training with notebooks. It evaluates skills in automating hyperparameter tuning to refine models for improved performance.
  • Module 3: Train and deploy models: In this topic, Microsoft Data Scientists develop the expertise to execute training scripts and implement efficient training pipelines. Participants learn to manage models effectively and deploy them using Azure services. The focus is on assessing proficiency in training processes, deployment strategies, and lifecycle management of machine learning models to ensure production-ready solutions that align with organizational goals.
  • Module 4: Optimize language models for AI applications: This advanced topic teaches Microsoft Data Scientists to enhance AI applications by optimizing language models. Skills are developed in preparing models for optimization, prompt engineering, and employing techniques like Prompt Flow and Retrieval Augmented Generation (RAG). Additionally, fine-tuning strategies are explored to maximize model performance.

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Q1. You manage an Azure Machine Learning workspace. The development environment for managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks. A Synapse Spark Compute is currently attached and uses system-assigned identity. You need to use Python code to update the Synapse Spark Compute to use a user-assigned identity. Solution: Pass the UserAssignedldentity class object to the SynapseSparkCompute class. Does the solution meet the goat?

A.Yes

B. No

Correct Answer: B

Q2. You manage an Azure Machine Learning workspace. The development environment for managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks. A Synapse Spark Compute is currently attached and uses system-assigned identity. You need to use Python code to update the Synapse Spark Compute to use a user-assigned identity. Solution: Initialize the DefaultAzureCredential class. Does the solution meet the goal?

A.Yes

B. No

Correct Answer: B

Q3. You manage an Azure Machine Learning workspace. You design a training job that is configured with a serverless compute. The serverless compute must have a specific instance type and count You need to configure the serverless compute by using Azure Machine Learning Python SDK v2. What should you do?

A.Specify the compute name by using the compute parameter of the command job

B. Configure the tier parameter to Dedicated VM.

C. Initialize and specify the ResourceConfiguration class

D. Initialize AmICompute class with size and type specification.

Correct Answer: C

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